SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4376–4400 of 15113 papers

TitleStatusHype
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul—0
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?—0
NeurIPS 2022 Competition: Driving SMARTS—0
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Parallel Automatic History Matching Algorithm Using Reinforcement Learning—0
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach—0
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility—0
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary—0
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Vector Quantized Encoding—0
CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality—0
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning—0
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement LearningCode0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy OptimizationCode0
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Emergency action termination for immediate reaction in hierarchical reinforcement learning—0
Controlling Commercial Cooling Systems Using Reinforcement Learning—0
Job Scheduling in Datacenters using Constraint Controlled RL—0
When is Realizability Sufficient for Off-Policy Reinforcement Learning?—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable ConceptsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified